The University of Alcalá (UAH), one of KantanMT’s Academic Partnersused the platform to teach final year undergraduate students Custom Machine Translation during the 2015-2016 academic year.

KantanMT.com was used in the course ‘Machine Translation and Post-editing,’ which was taught for the first time in the ‘Degree in Modern Languages Applied to Translation’ in UAH. English and Spanish were the main languages used during this course.

The KantanPEX Rule Editor enables members of KantanMT reduce the amount of manual post-editing required for a particular translation by creating, testing and deploying post-editing automation rules on their Machine Translation engines (client profiles).

The editor allows users to evaluate the output of a PEX (Post-Editing Automation) rule on a sample of translated content without needing to upload it to a client profile and run translation jobs. Users can enter up to three pairs of search and replace rules, which will be run in descending order on your content.

How to use the KantanMT PEX Rule Editor

Login into your KantanMT account using your email and your password.

You will be directed to the ‘Client Profiles’ tab in the ‘My Client Profiles’ page. The last profile you were working on will be ‘Active’ and marked in bold.

To use the ‘PEX-Rule Editor’ with a profile other than the ‘Active’ profile, click on the new profile name to select that profile for use with the ‘Kantan PEX-Rule editor’.

Then click the ‘KantanMT’ tab and select ‘PEX Editor’ from the drop-down menu.

You will be directed to the ‘PEX Editor’ page.

Type the content you wish to test on, in the ‘Test Content’ box.

Type the content you wish to search for in the ‘PEX Search Rules’ box.

Type what you want the replacement to be in the ‘PEX Replacement Rules’ box and click on the ‘Test PEX Rules’ button to test the PEX-Rules.

The results of your PEX-Rules will now appear in the ‘Output’ box.

Give the rules you have created a name by typing in the ‘Rule Name’ box.

Select the profile you wish to apply this rule(s) to and then click on the ‘Upload Rule’ button.

Additional Information

KantanMT PEX editor helps reduce the amount of manual post-editing required for a particular translation, hence, reducing project turn-around times and costs. For additional information on PEX-RULES and the Kantan PEX-Rule editor please click on the links below. For more details about KantanMT localization products and ways of improving work productivity and efficiency please contact us at info@kantanmt.com.

A good quality Machine Translation engine relies on the quality of the bilingual data used to train it. For most MT users, this bilingual data can be translated by humans, or it can be fully post-edited MT output. In both cases, the quality of the data will influence the engines quality. Selçuk Özcan, Transistent’s Co-founder will discuss the differences and give some tips for successful post-editing.Selçuk Özcan, Transistent’s Co-founder has given KantanMT permission to publish his blog post on Translation Quality. This post was originally published in Dragosfer and on the GALA Blog website.

We have entered a new age, and a new technology has come into play: Machine Translation (MT). It’s globally accepted that MT systems dramatically increase productivity but it’s a hard struggle to integrate this technology into your production process. Apart from handling the engine building and optimizing procedures, you have to transform your traditional workflow:

The traditional roles of the linguists (translators, editors, reviewers etc.) are reconstructed and converged to find a suitable place in this new, innovative workflow. The emerging role is called ‘post-edit’ and the linguists assigned to this role are called ‘post-editors’. You may want to recruit some willing linguists for this role, or persuade your staff to adopt a different point of view. But whatever the case may be, some training sessions are a must.

What are covered in training sessions?

1. Basic concepts of MT systems

Post-editors should have a notion of the dynamics of MT systems. It is important to focus on the system that is utilized (RBMT/SMT/Hybrid). For widely used SMT systems, it’s necessary for them to know:

how the systems behave

the functions of the Translation Model and Language Model*

input (given set of data) and output (raw MT output) relationship

what changes in different domains

* It’s not a must to give detailed information about that topics but touching on the issue will make a difference in determining the level of technical backgrounds of candidates. Some of the candidates may be included in testing team.

2. The characteristics of raw MT output

Post-editors should know the factors affecting MT output. On the other hand, the difference between working on fuzzy TM systems and with SMT systems has to be mentioned during a proper training session. Let’s try to figure out what to be given:

MT process is not the ‘T’ of the TEP workflow and raw MT output is not the target text expected to be output of ‘T’ process.

In the earlier stages of SMT engines, the output quality varies depending on the project’s dynamics and errors are not identical. As the system improves quality level becomes more even and consistent within the same domain.

There may be some word or phrase gaps in the systems’ pattern mappings. (Detecting these gaps is one of the main responsibilities of testing team but a successful post-editor must be informed about the possible gaps.)

3. Quality issues

This topic has two aspects: defining required target (end product) quality, and evaluation and estimation of output quality. The first one gives you the final destination and the second one makes you know where you are.

Required quality level is defined according to the project requirements but it mostly depends on target audience and intended usage of the target text. It seems similar to the procedure in TEP workflow. However, it’s slightly different; engine improvement plan should also be considered while defining the target quality level. Basically, this parameter is classified into two groups: publishable andunderstandable quality.

Evaluation and estimation aspect is a little bit more complicated. The most challenging factor is standardizing measurement metrics. Besides, the tools and systems used to evaluate and estimate the quality level have some more complex features. If you successfully establish your quality system, then adversities become easier to cope with.

It’s post-editors’duty to apprehend the dynamics of MT quality evaluation, and the distinction between MT and HT quality evaluation procedures. Thus, they are supposed to be aware of the expected error patterns. It will be more convenient to utilize the error categorization with your well-trained staff (QE staff and post-editors).

4. Post-editing Technique

The fourth and the last topic is the key to success. It covers appropriate method and principles, as well as the perspective post-editors usually acquire. Post-edit technique is formed using the materials prepared for the previous topics and the data obtained from the above mentioned procedures, and it is separately defined for almost every individual customized engines.

The core rule for this topic is that post-edit technique, as a concept, is likely to be definitely differentiated from traditional edit and/or review technique(s). Post-editors are likely to be capable of:

considering the post-edited data as a part of data set to be used in engine improvement, and performing his/her work accordingly.

applying the rules defined for the quality expectation levels.

As briefly described in topic #3, the distance between the measured output quality and required target quality may be seen as the post-edit distance. It roughly defines the post-editor’s tolerance and the extent to which he/she will perform his work. Other criterion allowing us to define the technique and the performance is the target quality group. If the target text is expected to be of publishable quality then it’s called full post-edit and otherwise light post-edit. Light & full post-edit techniques can be briefly defined as above but the distinction is not always so clear. Besides, under/over edit concepts are likely to be included to above mentioned issues. You may want to include some more details about these concepts in the post-editor training sessions; enriching the training materials with some examples would be a great idea!

About Selçuk Özcan

Selçuk Özcan has more than 5 years’ experience in the language industry and is a co-founder of Transistent Language Automation Services. He holds degrees in Mechanical Engineering and Translation Studies and has a keen interest in linguistics, NLP, language automation procedures, agile management and technology integration. Selçuk is mainly responsible for building high quality production models including Quality Estimation and deploying the ‘train the trainers’ model. He also teaches Computer-aided Translation and Total Quality Management at the Istanbul Yeni Yuzyil University, Translation & Interpreting Department.

Read More about KantanMT’s Partnership with Transistent in the official News Release, or if you are interested in joining the KantanMT Partner Program, contact Louise (info@kantanmt.com) for more details on how to get involved.

KantanMT started the New Year on a high note with the addition of the Turkish Language Service Provider, Transistent to the KantanMT Preferred MT Supplier partner program.

Selçuk Özcan, Transistent’s Co-founder has given KantanMT permission to publish his blog post on Translation Quality. This post was originally published in Dragosfer and the Transistent Blog.

Literally, the word quality has several meanings, one of them being “a high level of value or excellence” according to Merriam-Webster’s dictionary. How should one deal with this idea of “excellence” when the issue at hand is translation quality? What is required, it looks like, is a more pragmatic and objective answer to the abovementioned question.

This brings us to the question “how could an approach be objective?” Certainly, the issue should be assessed through empirical findings. But how? We are basically in need of an assessment procedure with standardized metrics. Here, we encounter another issue; standardization of translation quality. From now on, we need to associate these concepts with the context itself in order to make them clear.

Monolingual issues

Bilingual issues

As it’s widely known, three sets of factors have an effect on the quality of the translation process in general. Basically, analyzing source text’s monolingual issues, target text’s monolingual issues and bilingual issues defines the quality of the work done. Nevertheless, the procedure should be based on the requirements of the domain, audience and linguistic structure of both languages (source and target); and in each step, this key question should be considered: ‘Does the TT serve to the intended purpose?’

We still have not dealt with the standardization and quality of acceptable TT’s. The concept of “acceptable translation” has always been discussed throughout the history of translation studies. No one is able to precisely explain the requirements. However, a further study on dynamic QA models needs to go into details.There are various QA approaches and models. For most of them, acceptable translation falls into somewhere between bad and good quality, depending on the domain and target audience. The quality level is measured through the translation error rates developed to assess MT outputs (BLEU, F-Measure and TER) and there are four commonly accepted quality levels; bad, acceptable, good and excellent.

The formula is so simple: the TT containing more errors is considered to be worse quality. However, the errors should be correlated with the context and many other factors, such as importance for the client, expectations of the audience and so on. These factors define the errors’ severity as minor, major, and critical. A robust QA model should be based upon accurate error categorization so that reliable results may be obtained.

We tried to briefly describe the concept of QA modeling. Now, let’s see what’s going on in practice. There are three publicly available QA models which inspired many software developers on their QA tool development processes. One of them is LISA (Localization Industry Standards Association) QA Model. The LISA Model is very well known in the localization and translation industry and many company-specific QA models have been derived from it.

The second one is J2450 standard that was generated by SAE (Society for Automotive Engineers) and the last one is EN15038 standard, approved by CEN (Comité Européen de Normalisation) in 2006. All of the above mentioned models are the static QA models. One should create his/her own frameworks in compliance with the demands of the projects. Nowadays, many of the institutes have been working on dynamic QA models (EU Commission and TAUS). These models enable creating different metrics for several translation/localization projects.

About Selçuk Özcan

Selçuk Özcan has more than 5 years’ experience in the language industry and is a co-founder of Transistent Language Automation Services. He holds degrees in Mechanical Engineering and Translation Studies and has a keen interest in linguistics, NLP, language automation procedures, agile management and technology integration. Selçuk is mainly responsible for building high quality production models including Quality Estimation and deploying the ‘train the trainers’ model. He also teaches Computer-aided Translation and Total Quality Management at the Istanbul Yeni Yuzyil University, Translation & Interpreting Department.

Read More about KantanMT’s Partnership with Transistent in the official News Release, or if you are interested in joining the KantanMT Partner Program, contact Louise (info@kantanmt.com) for more details on how to get involved.

KantanMT is delighted to republish, with permission a post on machine translation technology and internet security that was recently written by Joseph Wojowski. Joseph Wojowski is the Director of Operations at Foreign Credits and Chief Technology Officer at Morningstar Global Translations LLC.

Machine Translation Technology and Internet Security

An issue that seems to have been brought up once in the industry and never addressed again are the data collection methods used by Microsoft, Google, Yahoo!, Skype, and Apple as well as the revelations of PRISM data collection from those same companies, thanks to Edward Snowden. More and more, it appears that the industry is moving closer and closer to full Machine Translation Integration and Usage, and with interesting, if alarming, findings being reported on Machine Translation’s usage when integrated into Translation Environments, the fact remains that Google Translate, Microsoft Bing Translator, and other publicly-available machine translation interfaces and APIs store every single word, phrase, segment, and sentence that is sent to them.

Terms and Conditions

What exactly are you agreeing to when you send translation segments through the Google Translate or Bing Translator website or API?

1 – Google Terms and Conditions

Essentially, in using Google’s services, you are agreeing to permit them to store the segment to use for creating more accurate translations in the future, they can also publish, display, and distribute the content.

“When you upload, submit, store, send or receive content to or through our Services, you give Google (and those we work with) a worldwide license to use, host, store, reproduce, modify, create derivative works (such as those resulting from translations, adaptations or other changes we make so that your content works better with our Services), communicate, publish, publicly perform, publicly display and distribute such content.” (Google Terms of Service – 14 April 2014, accessed on 8 December 2014)

Oh, and did I mention that in using the service, the user is bearing all liability for“LOST PROFITS, REVENUES, OR DATA, FINANCIAL LOSSES OR INDIRECT, SPECIAL, CONSEQUENTIAL, EXEMPLARY, OR PUNITIVE DAMAGES.” (Google Terms of Service – 14 April 2014, accessed on 8 December 2014)

So if it is discovered that a client’s confidential content is also located on Google’s servers because of a negligent translator, that translator is liable for losses and Google relinquishes liability for distributing what should have been kept confidential.

Alright, that’s a lot of legal wording, not the best news, and a lot to take in if this is the first time you’re hearing about this. What about Microsoft Bing Translator?

In writing their services agreement, Microsoft got very tricky. They start out positively by stating that you own your own content.

“Except for material that we license to you that may be incorporated into your own content (such as clip art), we do not claim ownership of the content you provide on the services. Your content remains your content, and you are responsible for it. We do not control, verify, pay for, or endorse the content that you and others make available on the services.” (Microsoft Services Agreement – effective 19 October 2012, accessed on 8 December 2014)

“When you transmit or upload Content to the Services, you’re giving Microsoft the worldwide right, without charge, to use Content as necessary: to provide the Services to you, to protect you, and to improve Microsoft products and services.”(Microsoft Services Agreement – effective 19 October 2012, accessed on 8 December 2014)

So again with Bing, while they originally state that you own the content you submit to their services, they also state that in doing so, you are giving them the right to use the information as they see fit and (more specifically) to improve the translation engine.

How do these terms affect the translation industry, then?

The problem arises whenever translators are working with documents that contain confidential or restricted-access information. Aside from his/her use of webmail hosted by Microsoft, Google, Apple, etc. – which also poses a problem with confidentiality – contents of documents that are sent through free, public machine translation engines; whether through the website or API, are leaking the information the translator agreed to keep confidential in the Non-Disclosure Agreement (if established) with the LSP; a clear and blatant breach of confidentiality.

But I’m a professional translator and have been for years, I don’t use MT and no self-respecting professional translator would.

Well, yes and no; a conflict arises from that mode of thinking. In theory, yes, a professional translator should know better than to blindly use Machine Translation because of its inaccurate and often unusable output. A professional translator; however, should also recognize that with advancements in MT Technology, Machine Translation can be a very powerful tool in the translator’s toolbox and can, at times, greatly aid in the translation of certain documents.

The current state of the use of MT more echoes the latter than the former. In 2013 research conducted by Common Sense Advisory, 64% of the 239 people who responded to the survey reported that colleagues frequently use free Machine Translation Engines; 62% of those sampled were concerned about free MT usage.

In the November/December 2014 Issue of the ATA Chronicle, Jost Zetzsche relayed information on how users were using the cloud-based translation tool MemSource. Of particular interest are the Machine Translation numbers relayed to him by David Canek, Founder of MemSource. 46.2% of its around 30,000 users (about 13,860 translators) were using Machine Translation; of those, 98% were using the Google Translate or a variant of the Bing Translator API. And of still greater alarm, a large percentage of users using Bing Translator chose to employ the “Microsoft with Feedback” option which sends the finalized target segment back to Microsoft (a financially appealing option since when selected, use of the API costs nothing).

As you can imagine, while I was reading that article, I was yelling at all 13.9 thousand of them through the magazine. How many of them were using Google or Bing MT with documents that should not have been sent to either Google or Microsoft? How many of these users knew to shut off the API for such documents – how many did?

There’s no way to be certain how much confidential information may have been leaked due to translator negligence, in the best scenario perhaps none, but it’s clear that the potential is very great.

On the other hand, in creating a tool as dynamic and ever-changing as a machine translation engine, the only way to train it and make it better is to use it, a sentiment that is echoed throughout the industry by developers of MT tools and something that can be seen in the output of Google translate over the past several years.

So what options are there for me to have an MT solution for my customers without risking a breach in confidentiality?

There are numerous non-public MT engines available – including Apertium, a developing open-source MT platform – however, none of them are as widely used (and therefore, as well-trained) as Google Translate or Bing Translator (yes, I realize that I just spent over 1,000 words talking about the risk involved in using Google Translate or Bing Translator).

So, is there another way? How can you gain the leverage of arguably the best-trained MT Engines available while keeping confidential information confidential?

There are companies who have foreseen this problem and addressed it, without pitching their product, here’s how it works. It acts as an MT API but before any segments are sent across your firewall to Google, it replaces all names, proper nouns, locations, positions, and numbers with an independent, anonymous token or placeholder. After the translated segment has returned from Google and is safely within the confines of your firewall, the potentially confidential material then replaces the tokens leaving you with the MT translated segment. On top of that, it also allows for customized tokenization rules to further anonymize sensitive data such as formulae, terminology, processes, etc.

While the purpose of this article was not to prevent translators from using MT, it is intended to get translators thinking about its use and increase awareness of the inherent risks and solution options available.

— Correction —

As I have been informed, the information in the original post is not as exact as it could be, there is a Microsoft Translator Privacy Agreement that more specifically addresses use of the Microsoft Translator. Apparently, with Translator, they take a sample of no more than 10% of “randomly selected, non-consecutive sentences from the text” submitted. Unused text is deleted within 48 hours after translation is provided.

If the user subscribes to their data subscriptions with a maximum of 250 million characters per month (also available at levels of 500 million, 635 million, and one billion) , he or she is then able to opt-out of logging.

There is also Microsoft Translator Hub which allows the user to personalize the translation engine where “The Hub retains and uses submitted documents in full in order to provide your personalized translation system and to improve the Translator service.” And it should be noted that, “After you remove a document from your Hub account we may continue to use it for improving the Translator service.”

***

So let’s analyze this development. 10% of the full text submitted is sampled and unused text is deleted within 48 hours of its service to the user. The text is still potentially from a sensitive document and still warrants awareness of the issue.

If you use The Translator Hub, it uses the full document to train the engine and even after you remove the document from your Hub, and they may also use it to continue improving the Translator service.

Now break out the calculators and slide rules, kids, it’s time to do some math.

In order to opt-out of logging, you need to purchase a data subscription of 250 million characters per month or more (the 250 million character level costs $2,055.00/month). If every word were 50 characters each, that would be 5 million words per month (where a month is 31 days) and a post-editor would have to process 161,290 words per day (working every single day of this 31-day month). It’s physically impossible for a post-editor to process 161,290 words in a day, let alone a month (working 8 hours a day for 20 days a month, 161,290 words per month would be 8,064.5 words per day). So we can safely assume that no freelance translator can afford to buy in at the 250 million character/month level especially when even in the busiest month, a single translator comes no where near being able to edit the amount of words necessary to make it a financially sound expense.

In the end, I still come to the same conclusion, we need to be more cognizant of what we send through free, public, and semi-public Machine Translation engines and educate ourselves on the risks associated with their use and the safer, more secure solutions available when working with confidential or restricted-access information.

The KantanMT team would like to thank Joseph Wojowski for allowing us to republish his very interesting and topical post on machine translation security. You can view the original post here.

At KantanMT, security, integrity and the privacy of our customers’ data is a top priority. We believe this is vital to their business operations and to our own success. Therefore, we use a multilayered approach to protect and encrypt this information. The KantanMT Data Privacy statement ensures that no client data is re-published, re-tasked or re-purposed and will also be fully encrypted during storage and transmission.

I’m new to machine translation and one of the things I’ve been doing at KantanMT is learning how to refine training data with a view to building stock engines.

Stock engines are the optional training data provided by KantanMT to improve the performance of your customized MT engine. In this post I’m going to describe the process of building an engine and refining the training data.

The building process on the platform is quite simple. From your dashboard on the website select “My Client Profiles” where you will find two profiles, which have already been set up. A default profile and sample profile; both of which let you run translation jobs straight away.

To create your own customized profile select ‘New’ at the top of the left-most column. This launches the client Profile Wizard. Enter the name of your new engine; try to make this something meaningful, or use an easily recognizable standard around how you name your profiles. This makes it easier to recognize which profile is which, when you have more than one profile.

When you select ‘next’ you will be asked to specify the source and target languages from drop down menus. The wizard lets you distinguish between different variants of the same language for example Canadian English or US English. Let’s say we’re translating from Canadian English to Canadian French. If you’re not sure which variant you need, have a quick look at the training data, which will give you the language codes.

The next step gives you an option to select a stock engine from a drop down menu. The stock engines are grouped according to their business area or domain.

You will see a summary of your choices, if you’re happy with them select ‘create’. Your new engine will be shown in the list of your client profiles. However, while you have created your engine, you haven’t yet built it.

Stock training data available for social and conversational domains on the KantanMT platform.

Building Your Engine

Selecting your profile from the list will make it the current active engine. By selecting the Training Data tab you can upload any additional training data easily by using the drag and drop function. Then select the ‘Build’ option to begin building your engine.

It’s always a good idea to supply as much useful training data as possible. This ‘educates’ the engine in the way your organization typically translates text.

Once the build job has been submitted, you can monitor its progress in the ‘My Jobs’ page.

When the job is completed the BuildAnalytics™ feature is created. This can be accessed by clicking on the database icon to the left of the profile name. BuildAnalytics will give you feedback on the strength of your engine using industry standard scores, as well as details about your engines word count. The tabs across the page will give you access to more detail.

The summary tab lets you to see the average BLEU, F-Measure and TER scores for the engine, and the pie charts show you a summary of the percentage scores for all segments. For more detail select the respective tabs and use the data to investigate individual segments.

KantanBuildAnalytics provides a granular analyis of your MT engine.

A Rejects Report is created for every file of Training Data uploaded. You can use this to determine why some of your data is not being used, and improve the uptake rate of your data.

Gap analysis gives you an effective way to improve your engine with relevant glossary or noise lists, which you can upload to future engine builds. By adding these terminology files in either TBX (Terminology Interchange) or XLSX (Microsoft Excel Spreadsheet) formats you will quickly improve the engines performance.

The Timeline tag shows you the evolution of your engine over its lifetime. This feature lets you compare the statistics with previous builds, and track all the data you have uploaded. On a couple of occasions, I used the archive feature to revert back to a previous build, when the engine building process was not going according to plan.

KantanMT Timeline lets you view your entire engine’s build history.

Improving Your Engine

A great way to improve your engines performance is to analyze the rejects report for the files with a higher rejection rate. Once you understand the reasons segments are rejected you can begin to address them. For example, an error 104 is caused by a difference in place holder counts. This can be something as simple as the source language using the % sign where the target language uses the word ‘percent’. In this case a preprocessor rule can be created to fix the problem.

A detailed rejects report shows you the errors in your MT engine.

A PEX rule editor is accessed from the KantanMT drop down menu. This lets you try out your preprocessor rules, and see the effect that they have in the data. I would suggest directly copying and pasting from the rejects report to the test area and applying your PEX rule to ensure you’re precisely targeting the data concerned. You can get instant feedback using this tool.

Once you’re happy with the way the rules work on the rejected data it’s useful to analyze the rest of the data to see what effect the rules will have. You want to avoid a situation where using a rule resolves 10 rejects, but creates 20 more. Once the rules are refined copy them to the appropriate files (source.ppx, target.ppx) and upload with the training data. Remember that the rules will run against the content in the order they are specified.

When you rebuild the engine they will be incorporated, and hopefully improve the scores.

Sue’s 3 Tips for Successfully Building MT Engines

Name your profiles clearly – When you are using a number of profiles simultaneously knowing what each one is (Language pair/domain) will make it much easier as you progress through the building process.

Take advantage of BuildAnalytics – Use the insights and Gap analysis features to give you tips on improving your engine. Listening to these tips can really help speed up the engine refinement process.

The PEX Rule Editor is your friend – Don’t be afraid to try out creating and using new PEX rules, if things go south you can always go back to previous versions of your engine.

My internship at KantanMT.com really opened my eyes to the world of language services and machine translation. Before joining the team I knew nothing about MT or the mechanics behind building engines. This was a great experience, and being part of such a smoothly run development team was an added bonus that I will take with me when I return ITB to finish my course.

About Sue McDermott

Sue is currently studying for a Diploma in Computer Science from ITB (Institute of Technology Blanchardstown). Sue joined KantanMT.com on a three month internship. She has a degree in English Literature and a background in business systems, and is also a full-time mum for the last 17 years.

Email: info@kantanmt.com, if you have any questions or want more information on the KantanMT platform.

KantanMT had an exciting year as it transitioned from a publicly funded business idea into a commercial enterprise that was officially launched in June 2013. The KantanMT team are delighted to have surpassed expectations, by developing and refining cutting edge technologies that make Machine Translation easier to understand and use.

Here are some of the highlights for 2013, as KantanMT looks back on an exceptional year.

Strong Customer Focus…

The year started on a high note, with the opening of a second office in Galway, Ireland, and KantanMT kept the forward momentum going as the year progressed. The Galway office is focused on customer service, product education and Customer Relationship Management (CRM), and is home to Aidan Collins, User Engagement Manager, Kevin McCoy, Customer Relationship Manager and MT Success Coach, and Gina Lawlor, Customer Relationship co-ordinator.

KantanMT officially launched the KantanMT Statistical Machine Translation (SMT) platform as a commercial entity in June 2013. The platform was tested pre-launch by both industry and academic professionals, and was presented at the European OPTIMALE (Optimizing Professional Translator Training in a Multilingual Europe) workshop in Brussels. OPTIMALE is an academic network of 70 partners from 32 European countries, and the organization aims to promote professional translator training as the translation industry merges with the internet and translation automation.

The KantanMT Community…

The KantanMT member’s community now includes top tier Language Service Providers (LSPs), multinationals and smaller organizations. In 2013, the community has grown from 400 members in January to 3400 registered members in December, and in response to this growth, KantanMT introduced two partner programs, with the objective of improving the Machine Translation ecosystem.

The Developer Partner Program, which supports organizations interested in developing integrated technology solutions, and the Preferred Supplier of MT Program, dedicated to strengthening the use of MT technology in the global translation supply chain. KantanMT’s Preferred Suppliers of MT are:

To date, the most popular target languages on the KantanMT platform are; French, Spanish and Brazilian-Portuguese. Members have uploaded more than 67 billion training words and built approx. 7,000 customized KantanMT engines that translated more than 500 million words.

As usage of the platform increased, KantanMT focused on developing new technologies to improve the translation process, including a mobile application for iOS and Android that allows users to get access to their KantanMT engines on the go.

KantanMT’s Core Technologies from 2013…

KantanMT have been kept busy continuously developing and releasing new technologies to help clients build robust business models to integrate Machine Translation into existing workflows.

TotalRecall™ – combines TM and MT technology, TM matches with a ‘fuzzy match’ score of less than 85% are automatically put through the customized MT engine, giving the users the benefits of both technologies.

KantanISR™ Instant Segment Retraining technology that allows members near instantaneous correction and retraining of their KantanMT engines.

Kantan API – critical for the development of software connectors and smooth integration of KantanMT into existing translation workflows. The success of the MemoQ connector, led to the development of subsequent connectors for MemSource and XTM.

KantanMT sourced and cleaned a range of bi-directional domain specific stock engines that consist of approx. six million words across legal, medical and financial domains and made them available to its members. KantanMT also developed support for Traditional and Simplified Chinese, Japanese, Thai and Croatian Languages during 2013.

Recognition as Business Innovators…

KantanMT received awards for business innovation and entrepreneurship throughout the year. Founder and Chief Architect, Tony O’Dowd was presented with the ICT Commercialization award in September.

In October, KantanMT was shortlisted for the PITCH start-up competition and participated in the ALPHA Program for start-ups at Dublin’s Web Summit, the largest tech conference in Europe. Earlier in the year KantanMT was also shortlisted for the Vodafone Start-up of the Year awards.

KantanMT were silver sponsors at the annual 2013 ASLIB Conference ‘Adopting the theme Translating and the Computer’ that took place in London, in November, and in October, Tony O’Dowd, presented at the TAUS Machine Translation Showcase at Localization World in Silicon Valley.

KantanMT have recently published a white paper introducing its cornerstone Quality Estimation technology, KantanAnalytics, and how this technology provides solutions to the biggest industry challenges facing widespread adoption of Machine Translation.